As artificial intelligence (AI) agents carry out longer chains of tasks, oversight must establish whether their behavior still serves an authorized goal. Here, we introduce the anchor: the point at which reference information and human-authorized standards enter an autonomous system. We argue that oversight design should first identify the reference needed to assess goal drift and whether the system's internal record can supply it, then decide who checks and how often, and finally specify who may revise the goal. Goal drift can be impossible to certify from a system's internal record. The missing reference must come from outside that record. Combining a reliability model of checkpoint spacing with evidence from training, execution, and multi-agent coordination, we develop a task taxonomy to guide anchor placement and decisions about which checks to automate. For AI systems that serve human purposes, human judgment is an ineliminable structural requirement in authorizing and revising the purposes and standards that govern autonomous operation, even when routine checking and execution are automated.

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